QC/QA Data Flagging Application

This repository hosts a Dash Plotly data flagging app for ACSM data structured in HDF5 format using the DIMA submodule. The provided Jupyter notebooks walk you through the steps to append metadata about diagnostic and target channels, which are necessary for the app to run properly.

Getting Started

Requirements

For Windows users, the following are required:

Git Bash: Git Bash will be used to run shell scripts (.sh files).
Conda: You must have Anaconda or Miniconda installed on your system. Git Bash needs access to Conda to set up the environment properly. Ensure that Conda is added to your system’s PATH during installation.
PSI Network Access (for data retrieval): Real data retrieval can only be performed when connected to the PSI network and with the appropriate access rights to the source network drive.

Clone the Repository

Open Git Bash and run:

cd Gitea
git clone --recurse-submodules https://gitea.psi.ch/apog/acsmnode.git
cd acsmnode

Run the Data Chain App

Open PowerShell as Administrator and navigate to the acsmnode repository.
Create a .env file in the root of acsmnode/.
Securely store your network drive access credentials in the .env file by adding the following lines:
```
CIFS_USER=<your-username>
CIFS_PASS=<your-password>
```
To protect your credentials:
- Do not share the .env file with others.
- Ensure the file is excluded from version control by adding .env to your .gitignore and .dockerignore files.

Open Docker Desktop, then build the container image:

docker build -f Dockerfile.acsmchain -t datachain_processor .

Run the app:

docker compose --file docker-compose.yaml up datachain_processor

Access:
- Jupyter Lab: http://localhost:8889/lab/tree/notebooks/
Stop the app: In the previously open PowerShell terminal, enter:
```
Ctrl + C
```
After the container is properly Stopped, remove the container process as:
```
docker rm $(docker ps -aq --filter ancestor=datachain_processor)
```

Set Up the Python Environment

If Git Bash lacks a suitable Python interpreter, run:

   bash env_setup.sh

Run the Dashboard App

Run the following command to start the dashboard app:

python data_flagging_app.py

This command will launch the data flagging app.

Stop the Dashboard App

Run the following command to stop the dashboard app:

CTRL + C

This command will terminate the server process running the app.

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